The authentication step requires that an application request an OAuth 2.0 access token at runtime. With Azure AD, access to a resource is a two-step process:įirst, the security principal's identity is authenticated and an OAuth 2.0 token is returned. When a security principal (a user, group, or application) attempts to access a blob resource, the request must be authorized, unless it's a blob available for anonymous access. For more information, see Grant limited access to data with shared access signatures. Only storage accounts created with the Azure Resource Manager deployment model support Azure AD authorization.īlob storage additionally supports creating shared access signatures (SAS) that are signed with Azure AD credentials. Microsoft recommends using Azure AD authorization with your blob applications when possible to assure access with minimum required privileges.Īuthorization with Azure AD is available for all general-purpose and Blob storage accounts in all public regions and national clouds. The token can then be used to authorize a request against the Blob service.Īuthorization with Azure AD provides superior security and ease of use over Shared Key authorization. The security principal is authenticated by Azure AD to return an OAuth 2.0 token. With Azure AD, you can use Azure role-based access control (Azure RBAC) to grant permissions to a security principal, which may be a user, group, or application service principal. Use the Pipeline parameter as Foreach Items settingĮdit the “ForEach” loop, add a Copy activity followed by configuring both Source and Sink:įor the Sink configuration, I am using the Auto-Create table option and adding a Drop table statement as a Pre-Copy script:Īdd dynamic TABLE IF EXISTS ‘,item().Azure Storage supports using Azure Active Directory (Azure AD) to authorize requests to blob data. The parameter type is Array, and the value is a JSON string containing the names of the sheets: To achieve this, I have created a parameter for the pipeline. The “ForEach” activity will iterate through all sheets and copy their content into a table. Create a new pipeline and add a “ForEach” activity.I will be creating a table per Excel sheet under the dbo schema. Similarly, as for the source dataset, create a parameter to hold the table name. Create another dataset for the destination database but this time selecting the Azure SQL Database as a dataset type.One of the advantages of using parameters is reusability, and I will leverage that in this case, as ADF will iterate through all sheets available in the Excel file. As I mentioned earlier, the excel file has two sheets, the first one has the rates, and the second one has the currency names and codes. In addition, I created a parameter to hold the sheet’s name. Navigate to the Dataset page and create a dataset for Azure Data Lake Storage Gen2 by selecting the excel file.Create a Linked Service for the Azure SQL Database.Create a new Linked Service for Azure Data Lake Storage Gen2.The following procedure outlines the required configuration: Pipeline: It is the logical workflow of data transfer activities. Linked Services: Contains the source connection details and credentials.ĭatasets: Represents a named logical view of the source data. To do this, the following ADF components are needed: I’ll be using the Copy Activity for the data transfer. Now is the time to build and configure the ADF pipeline. The table structure will reflect both the header and columns within each sheet. I will configure the ADF pipeline to create one table per sheet. As you can see, there are no tables created yet. This database will host the Exchange Rate data. I provisioned an Azure SQL database called One51Training. The “Data” sheet contains exchange rates per date for different currencies, while the “Note” sheet has the full list of currencies with their codes and names. In this workbook, there are two sheets, “Data” and “Note”. I have an excel workbook titled ‘2018-2020.xlsx’ sitting in Azure Data Lake Gen2 under the “excel dataset” folder. In this post, I will develop an ADF pipeline to load an excel file from Azure Data Lake Gen 2 into an Azure SQL Database. Prior to ADF supporting such functionality, data engineers needed to apply workarounds, such as using PowerShell scripts or Azure Functions to convert the excel file into CSV. These files could be located in different places, including as Amazon S3, Amazon S3 Compatible Storage, Azure Blob, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, Azure File Storage, File System, FTP/SFTP, Google Cloud Storage, HDFS, HTTP and Oracle Cloud Storage. Azure Data Factory (ADF) now has built-in functionality that supports ingesting data from xls and xlsx files.
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